{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "49ecbd51-e7ce-47aa-9643-61c8d95ea405",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "葡萄酒数据集\n",
      "     label     a1    a2    a3    a4   a5    a6    a7    a8    a9    a10   a11  \\\n",
      "0        1  14.23  1.71  2.43  15.6  127  2.80  3.06  0.28  2.29   5.64  1.04   \n",
      "1        1  13.20  1.78  2.14  11.2  100  2.65  2.76  0.26  1.28   4.38  1.05   \n",
      "2        1  13.16  2.36  2.67  18.6  101  2.80  3.24  0.30  2.81   5.68  1.03   \n",
      "3        1  14.37  1.95  2.50  16.8  113  3.85  3.49  0.24  2.18   7.80  0.86   \n",
      "4        1  13.24  2.59  2.87  21.0  118  2.80  2.69  0.39  1.82   4.32  1.04   \n",
      "..     ...    ...   ...   ...   ...  ...   ...   ...   ...   ...    ...   ...   \n",
      "173      3  13.71  5.65  2.45  20.5   95  1.68  0.61  0.52  1.06   7.70  0.64   \n",
      "174      3  13.40  3.91  2.48  23.0  102  1.80  0.75  0.43  1.41   7.30  0.70   \n",
      "175      3  13.27  4.28  2.26  20.0  120  1.59  0.69  0.43  1.35  10.20  0.59   \n",
      "176      3  13.17  2.59  2.37  20.0  120  1.65  0.68  0.53  1.46   9.30  0.60   \n",
      "177      3  14.13  4.10  2.74  24.5   96  2.05  0.76  0.56  1.35   9.20  0.61   \n",
      "\n",
      "      a12   a13  \n",
      "0    3.92  1065  \n",
      "1    3.40  1050  \n",
      "2    3.17  1185  \n",
      "3    3.45  1480  \n",
      "4    2.93   735  \n",
      "..    ...   ...  \n",
      "173  1.74   740  \n",
      "174  1.56   750  \n",
      "175  1.56   835  \n",
      "176  1.62   840  \n",
      "177  1.60   560  \n",
      "\n",
      "[178 rows x 14 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "names=['label','a1','a2','a3','a4','a5','a6','a7','a8','a9','a10','a11','a12','a13']\n",
    "datase=pd.read_csv(\"wine.data\",names=names)\n",
    "print(\"葡萄酒数据集\")\n",
    "print(datase)\n",
    "data=datase.iloc[range(0,178),range(1,14)]\n",
    "target=datase.iloc[range(0,178),range(0,1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e908503a-6a2b-4f62-9f6d-f7588671151c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "列 1 中异常值: []\n",
      "列 2 中异常值: [5.8 5.51 5.65]\n",
      "列 3 中异常值: [1.36 3.22 3.23]\n",
      "列 4 中异常值: [10.6 30.0 28.5 28.5]\n",
      "列 5 中异常值: [151.0 139.0 136.0 162.0]\n",
      "列 6 中异常值: []\n",
      "列 7 中异常值: []\n",
      "列 8 中异常值: []\n",
      "列 9 中异常值: [3.28 3.58]\n",
      "列 10 中异常值: [10.8 13.0 11.75 10.68]\n",
      "列 11 中异常值: [1.71]\n",
      "列 12 中异常值: []\n",
      "列 13 中异常值: []\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x1000 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 修正参数配置拼写\n",
    "#plt.style.use('seaborn-darkgrid')\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 解决负号显示问题\n",
    "\n",
    "# 假设 data 是已加载的 DataFrame（需用户补充数据加载步骤）\n",
    "# data = pd.read_csv(\"your_data.csv\")  # 示例：加载数据\n",
    "\n",
    "# 绘制箱线图（子图布局调整为 (4,4) 以容纳13列）\n",
    "data.plot(kind='box', subplots=True, layout=(4,4), sharex=False, sharey=False, figsize=(12,10))\n",
    "\n",
    "# 获取箱线图异常值（使用 return_type='dict'）\n",
    "p = data.boxplot(return_type='dict')\n",
    "\n",
    "# 循环打印异常值（使用 len(p['fliers']) 适配实际列数）\n",
    "for i in range(len(p['fliers'])):\n",
    "    y = p['fliers'][i].get_ydata()  # 获取异常值数据\n",
    "    print(f'列 {i+1} 中异常值: {y}')\n",
    "\n",
    "plt.tight_layout()  # 自动调整子图布局\n",
    "plt.show()  # 显示图像"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "186483bb-ce70-4bc7-bb67-e0929c8464bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.51861254 -0.5622498   0.23205254 ...  0.36217728  1.84791957\n",
      "   1.01300893]\n",
      " [ 0.24628963 -0.49941338 -0.82799632 ...  0.40605066  1.1134493\n",
      "   0.96524152]\n",
      " [ 0.19687903  0.02123125  1.10933436 ...  0.31830389  0.78858745\n",
      "   1.39514818]\n",
      " ...\n",
      " [ 0.33275817  1.74474449 -0.38935541 ... -1.61212515 -1.48544548\n",
      "   0.28057537]\n",
      " [ 0.20923168  0.22769377  0.01273209 ... -1.56825176 -1.40069891\n",
      "   0.29649784]\n",
      " [ 1.39508604  1.58316512  1.36520822 ... -1.52437837 -1.42894777\n",
      "  -0.59516041]]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn import preprocessing  # 修正拼写错误：preprpcessing → preprocessing\n",
    "\n",
    "# 修正变量名：datase → dataset\n",
    "names = ['label', 'a1', 'a2', 'a3', 'a4', 'a5', 'a6', 'a7', 'a8', 'a9', 'a10', 'a11', 'a12', 'a13']\n",
    "dataset = pd.read_csv(\"wine.data\", names=names)\n",
    "\n",
    "# 提取特征和目标变量（简化target提取方式）\n",
    "data = dataset.iloc[:, 1:]  # 所有行，第1列及之后（特征）\n",
    "target = dataset.iloc[:, 0].values  # 所有行，第0列（标签）\n",
    "\n",
    "# 标准化处理（修正类名和方法名：STTANDARDsCALER → StandardScaler，fit_transfrom → fit_transform）\n",
    "scaler = preprocessing.StandardScaler()\n",
    "cdata = scaler.fit_transform(data)  # 修正变量名：cadata → cdata\n",
    "\n",
    "print(cdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7322254a-80f8-460b-9282-043241c9d8af",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt  # 补充导入matplotlib（原代码缺少）\n",
    "from sklearn.neighbors import KNeighborsClassifier  # 修正模块名和类名拼写\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "\n",
    "# 假设cdata和target已通过前文标准化代码生成（需确保cdata和target定义正确）\n",
    "# x, y = cdata, target  # 若已定义，此行可保留\n",
    "\n",
    "# ---------------------- 修正核心错误 ----------------------\n",
    "k_range = range(1, 15)\n",
    "k_error = []\n",
    "\n",
    "for k in k_range:  # 修正变量名大小写（K_range → k_range）\n",
    "    model = KNeighborsClassifier(n_neighbors=k)  # 修正类名拼写\n",
    "    # 交叉验证：5折，评估准确率（修正参数分隔符和变量名）\n",
    "    scores = cross_val_score(model, x, y, cv=5, scoring='accuracy')  \n",
    "    k_error.append(1 - scores.mean())  # 计算平均误差（1-准确率）\n",
    "\n",
    "# ---------------------- 修正Matplotlib绘图 ----------------------\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 设置中文字体（修正参数名和格式）\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 解决负号显示问题（可选）\n",
    "\n",
    "plt.plot(k_range, k_error, marker='o')  # 绘制k值与误差的关系（补充绘图逻辑）\n",
    "plt.xlabel('k值（近邻个数）')  # 修正xlabel拼写（原“花瓣长度”与逻辑无关，此处改为k值）\n",
    "plt.ylabel('误差（1-准确率）')  # 修正ylabel拼写和内容\n",
    "plt.title('K值与交叉验证误差的关系')  # 补充标题（可选）\n",
    "plt.show()  #"
   ]
  }
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